class_docstr: Class to imitate gensim's KeyedVectors, but instead getting the vectors from the memory,
  the vectors will be retrieved from a redis db.
  Based on https://engineering.talentpair.com/serving-word-vectors-
  for-distributed-computations-c5065cbaa02f
class_docstr_tok: ["Class","to","imitate","gensim","'s","KeyedVectors",",","but","instead","getting","the","vectors","from","the","memory",",","the","vectors","will","be","retrieved","from","a","redis","db",".","Based","on","https",":","\/\/engineering.talentpair.com\/serving-word-vectors-","for-distributed-computations-c5065cbaa02f"]
docstr: Returns numpy array for single word or vstack for multiple words
docstr_tok: ["Returns","numpy","array","for","single","word","or","vstack","for","multiple","words"]
code: def __getitem__(self, words):
      """
      Returns numpy array for single word or vstack for multiple words
      """
      if isinstance(words, str):
          return self.word_vec(words)
      return np.vstack([self.word_vec(word) for word in words])
  
file_path: textpipe/textpipe/wrappers.py
imports: import pickle
  from functools import lru_cache
  from urllib.parse import urlparse
  import numpy
  from gensim.models.keyedvectors import KeyedVectors
  from redis import Redis
  from redis.exceptions import RedisError
  from tqdm import tqdm
label_desc: Use image node_id 3 for calling the RedisKeyedVectors obj's underlying member method code with example usage: obj.__getitem__(words) and returns: np, self